Percussionist Owen Douglas Pearl is trading his drum sticks for a plane ticket, traveling to Korea to share his novel Raman spectroscopy research, after studying in China last year.

Third-year student Owen Pearl (ME) is a member of the Temple University Percussion Ensemble and the drummer of indie rock band, Float Upstream, so rhythm is something that comes naturally. That could explain how he became so adept at Raman spectroscopy—and research that is taking him overseas for the second time in as many years.

After studying abroad in China last year, Pearl was named Honorable Mention for the prestigious Goldwater Scholarship. He was also a member of an interdisciplinary team of students that won the 24-hour think-tank style SPARK​ competition​. Now, he is heading to Korea to present the work that he pioneered while at Xiamen University in China and continued with Temple Engineering Associate Professor Dmitriy Dikin on evaluating spectral correlation and the reproducibility of Raman signals.

Since its discovery in 1928, Raman spectroscopy has been applied to numerous commercial applications, from pharmaceutical quality assurance to the detection of explosives in airports. The technique observes vibrational, rotational, and other low-frequency modes in a system—in this case molecules or a molecule. It relies on inelastic (Raman) scattering of monochromatic light, usually from a laser in the visible, near infrared, or near ultraviolet range. The laser light interacts with molecular vibrations, phonons or other excitement in the system, resulting in the energy of the laser photons being shifted up or down.

Pearl's Band, Float Upstream, playing in Hershey.

Pearl’s research posits a method of finding the likelihood that a feature on a recorded spectrum is the result of random noise in a measurement system.

“By measuring the same exact sample under the same exact conditions, you would expect your data to be identical,” he said. “However, random noise causes variation between the two measurements according to a normal distribution.”

In other words, he’s finding a rhythm among what could be construed as noise, or useless data.

“I created a fully coded method that automatically compares the difference, point by point, between a data point on a graph and its corresponding point on the supposedly identical graph, to predict if a feature is really there, or just fake,” he said.

As he described it, there could be “limitless application” in any signal processing requirement where multiple measurements (even if noisy) are an option. It also does not directly modify the data by averaging or smoothing or anything. It simply provides a way to better interpret the existing data.